Topographical models of geographical areas may be used for many different applications. For example, topographical models may be used for flight simulators, urban planning, disaster preparedness and analysis, mapping, and situational awareness (i.e. monitoring a given geographical area of a period of time).
One common topographical model is the digital elevation map (DEM). A DEM is a sampled matrix representation of a geographical area that may be generated in an automated fashion by a computer. In a DEM, coordinate points are made to correspond with a height value.
There are two types of elevation models: a digital terrain model, and a digital surface model. A digital terrain model is also known as a bare earth model, which is a DEM that contains no manmade objects or vegetation. A digital surface model is also known as a reflective surface model, which is a DEM that contains manmade objects and vegetation.
Regions of interest (ROI) are specific objects within an area of interest (AOI) that are being modeled. Regions of interest are also known as regions, and include manmade objects and vegetation, such as buildings, aircraft, boats and different types of terrain.
User demand for three-dimensional (3D) models has grown steadily over the past several years. More recently, the need for “time critical” 3D models for situational awareness has become the more common need. Current model generation methods may not be cost effective and may require a considerable amount of processing time/resources, which makes these products impractical for some users. Indeed, the lengthy turnaround times for 3D models often preclude some situational awareness applications, such as tracking the location of a given vehicle or object, or identifying the vehicle or object.
Attempts at designing image processing applications that track the locations of, and information about, specific regions of interest or objects have been made. For example, U.S. Pat. No. 5,974,201 to Chang et al. discloses an active information system based upon the concept of smart images. Image information or data is provided with an associated knowledge structure, comprising protocols, hot spots, active indexes, and attributes, to thereby provide a smart image or smart images as the base for the active image information system. The smart images provide the system the ability of automating various operations in a given environment, and the images themselves automatically respond to environment changes. The smart images also allow for the active indexing of hot spots, of points of interest. Protocols serve as a user-system interaction model, and the hot spots and active indexes serve as a task model, in the active information system. As a result, the smart image system produces images as active objects with distributed knowledge.
A further attempt at such an image processing application is disclosed in U.S. Pat. No. 7,499,590 to Seeber. Seeber discloses a system for discovering from a database an object which is confusingly similar with a known object. A database is searched for objects which, when discovered, may be duplicated and stored. A determination is then made if any object from the database is confusingly similar with a known object.
Existing systems may not provide adequate analysis of certain image types and geographic models. As such, further image processing systems may be desirable.